Q4NX is FastFlowLM's native packed-quantization format - a rearranged Q4_1
layout tuned for the NPU matrix engine's tile sizes and memory access
patterns. It is
not
a GGUF file and it does not run on llama.cpp or
Ollama; it is meant exclusively for the
FastFlowLM
engine on AMD Ryzen AI NPUs.
Requirements
FastFlowLM >= 0.9.45 (
flm
CLI)
AMD Ryzen AI processor with
XDNA2 (NPU2)
- Strix Point / Ryzen AI 300
series or later
XRT NPU stack installed
32 GB of unified system memory (Q4NX weights + activations + KV cache)
Files
File
Purpose
model.q4nx
Quantized Q4NX weights
config.json
FastFlowLM model configuration
tokenizer.json
Tokenizer
tokenizer_config.json
Special tokens and chat template
chat_template.jinja
Chat template (optional)
flm-add.py
Installer script - registers this model with FastFlowLM
Install and run
This repository works with
flm-add.py
, a small installer that copies the model
into the FastFlowLM user directory and registers the tag
darwin-opus:36b
. It never
modifies the system FastFlowLM install.
FastFlowLM's NPU kernels (xclbins) are closed source and are not shipped in
this repository.
flm-add.py
links the kernels of the official
qwen3.6-moe:35b-a3b
model (
Qwen3.6-35B-A3B-NPU2
), because this model shares the same engine family
(
qwen3.6-moe
) and architecture.
Model
Registry tag:
aquila-mini-moe:35b-a3b
Engine family:
qwen3.6-moe
Kernel source: Qwen3.6-35B-A3B-NPU2
Original model card
See the upstream model card for training details, benchmarks, and upstream
usage. This repository only contains the Q4NX conversion for FastFlowLM.
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